Overview

Dataset statistics

Number of variables17
Number of observations17544
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 MiB
Average record size in memory136.0 B

Variable types

NUM10
BOOL3
CAT3
DATE1

Warnings

dteday has a high cardinality: 731 distinct values High cardinality
atemp is highly correlated with tempHigh correlation
temp is highly correlated with atempHigh correlation
cnt is highly correlated with registeredHigh correlation
registered is highly correlated with cntHigh correlation
dteday is uniformly distributed Uniform
datetime has unique values Unique
hr has 731 (4.2%) zeros Zeros
weekday has 2520 (14.4%) zeros Zeros
windspeed has 2183 (12.4%) zeros Zeros
casual has 1623 (9.3%) zeros Zeros

Reproduction

Analysis started2020-12-03 13:32:01.911632
Analysis finished2020-12-03 13:32:21.356878
Duration19.45 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

datetime
Date

UNIQUE

Distinct17544
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size137.1 KiB
Minimum2011-01-01 00:00:00
Maximum2012-12-31 23:00:00
2020-12-03T13:32:21.453765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:21.663311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

dteday
Categorical

HIGH CARDINALITY
UNIFORM

Distinct731
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size137.1 KiB
2012-07-14
 
24
2011-10-25
 
24
2012-04-06
 
24
2012-11-01
 
24
2012-05-09
 
24
Other values (726)
17424 
ValueCountFrequency (%) 
2012-07-14240.1%
 
2011-10-25240.1%
 
2012-04-06240.1%
 
2012-11-01240.1%
 
2012-05-09240.1%
 
2012-03-20240.1%
 
2011-11-19240.1%
 
2012-06-28240.1%
 
2011-06-10240.1%
 
2011-12-09240.1%
 
Other values (721)1730498.6%
 
2020-12-03T13:32:21.903341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-03T13:32:22.148235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

season
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size137.1 KiB
3
4512 
2
4416 
1
4344 
4
4272 
ValueCountFrequency (%) 
3451225.7%
 
2441625.2%
 
1434424.8%
 
4427224.4%
 
2020-12-03T13:32:22.265352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-03T13:32:22.367755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:22.480631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

yr
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size137.1 KiB
1
8784 
0
8760 
ValueCountFrequency (%) 
1878450.1%
 
0876049.9%
 
2020-12-03T13:32:22.566222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

mnth
Real number (ℝ≥0)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.519835841
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size137.1 KiB
2020-12-03T13:32:22.658160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.449649205
Coefficient of variation (CV)0.529100623
Kurtosis-1.209056868
Mean6.519835841
Median Absolute Deviation (MAD)3
Skewness-0.008132615041
Sum114384
Variance11.90007964
MonotocityNot monotonic
2020-12-03T13:32:22.784500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
1214888.5%
 
1014888.5%
 
814888.5%
 
714888.5%
 
514888.5%
 
314888.5%
 
114888.5%
 
1114408.2%
 
914408.2%
 
614408.2%
 
Other values (2)280816.0%
 
ValueCountFrequency (%) 
114888.5%
 
213687.8%
 
314888.5%
 
414408.2%
 
514888.5%
 
ValueCountFrequency (%) 
1214888.5%
 
1114408.2%
 
1014888.5%
 
914408.2%
 
814888.5%
 

hr
Real number (ℝ≥0)

ZEROS

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros731
Zeros (%)4.2%
Memory size137.1 KiB
2020-12-03T13:32:22.941102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.922383842
Coefficient of variation (CV)0.601946421
Kurtosis-1.204175095
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum201756
Variance47.91939805
MonotocityNot monotonic
2020-12-03T13:32:23.101736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%) 
157314.2%
 
147314.2%
 
167314.2%
 
17314.2%
 
177314.2%
 
27314.2%
 
187314.2%
 
37314.2%
 
197314.2%
 
47314.2%
 
Other values (14)1023458.3%
 
ValueCountFrequency (%) 
07314.2%
 
17314.2%
 
27314.2%
 
37314.2%
 
47314.2%
 
ValueCountFrequency (%) 
237314.2%
 
227314.2%
 
217314.2%
 
207314.2%
 
197314.2%
 

holiday
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size137.1 KiB
0
17022 
1
 
522
ValueCountFrequency (%) 
01702297.0%
 
15223.0%
 
2020-12-03T13:32:23.217253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

weekday
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.002735978
Minimum0
Maximum6
Zeros2520
Zeros (%)14.4%
Memory size137.1 KiB
2020-12-03T13:32:23.298139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.003472285
Coefficient of variation (CV)0.6672155991
Kurtosis-1.253931627
Mean3.002735978
Median Absolute Deviation (MAD)2
Skewness-0.002736202689
Sum52680
Variance4.013901196
MonotocityNot monotonic
2020-12-03T13:32:23.419797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
6252014.4%
 
5252014.4%
 
0252014.4%
 
4249614.2%
 
3249614.2%
 
2249614.2%
 
1249614.2%
 
ValueCountFrequency (%) 
0252014.4%
 
1249614.2%
 
2249614.2%
 
3249614.2%
 
4249614.2%
 
ValueCountFrequency (%) 
6252014.4%
 
5252014.4%
 
4249614.2%
 
3249614.2%
 
2249614.2%
 

workingday
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size137.1 KiB
1
11982 
0
5562 
ValueCountFrequency (%) 
11198268.3%
 
0556231.7%
 
2020-12-03T13:32:23.527209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

weathersit
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size137.1 KiB
1
11455 
2
4563 
3
1526 
ValueCountFrequency (%) 
11145565.3%
 
2456326.0%
 
315268.7%
 
2020-12-03T13:32:23.631938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-03T13:32:23.727251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:23.832500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

temp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct165
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4950450296
Minimum0.02
Maximum1
Zeros0
Zeros (%)0.0%
Memory size137.1 KiB
2020-12-03T13:32:23.987770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.2
Q10.34
median0.5
Q30.66
95-th percentile0.8
Maximum1
Range0.98
Interquartile range (IQR)0.32

Descriptive statistics

Standard deviation0.1931912655
Coefficient of variation (CV)0.3902498843
Kurtosis-0.9457101585
Mean0.4950450296
Median Absolute Deviation (MAD)0.16
Skewness-0.0005553412992
Sum8685.07
Variance0.03732286508
MonotocityNot monotonic
2020-12-03T13:32:24.188563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.627274.1%
 
0.666934.0%
 
0.646923.9%
 
0.76903.9%
 
0.66753.8%
 
0.366713.8%
 
0.346463.7%
 
0.36423.7%
 
0.46143.5%
 
0.326143.5%
 
Other values (155)1088062.0%
 
ValueCountFrequency (%) 
0.02180.1%
 
0.031< 0.1%
 
0.04170.1%
 
0.06170.1%
 
0.08170.1%
 
ValueCountFrequency (%) 
11< 0.1%
 
0.981< 0.1%
 
0.96160.1%
 
0.94170.1%
 
0.92490.3%
 

atemp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct178
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4739600433
Minimum0
Maximum1
Zeros2
Zeros (%)< 0.1%
Memory size137.1 KiB
2020-12-03T13:32:24.380621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.197
Q10.3333
median0.4848
Q30.6212
95-th percentile0.7424
Maximum1
Range1
Interquartile range (IQR)0.2879

Descriptive statistics

Standard deviation0.1725143726
Coefficient of variation (CV)0.3639850554
Kurtosis-0.8529985481
Mean0.4739600433
Median Absolute Deviation (MAD)0.1364
Skewness-0.08493250559
Sum8315.155
Variance0.02976120876
MonotocityNot monotonic
2020-12-03T13:32:24.569268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.62129885.6%
 
0.51526193.5%
 
0.40916143.5%
 
0.33336013.4%
 
0.66675933.4%
 
0.60615883.4%
 
0.53035793.3%
 
0.55753.3%
 
0.45455603.2%
 
0.3035503.1%
 
Other values (168)1127764.3%
 
ValueCountFrequency (%) 
02< 0.1%
 
0.01524< 0.1%
 
0.022751< 0.1%
 
0.03038< 0.1%
 
0.03791< 0.1%
 
ValueCountFrequency (%) 
11< 0.1%
 
0.98482< 0.1%
 
0.95451< 0.1%
 
0.92425< 0.1%
 
0.90915< 0.1%
 

hum
Real number (ℝ≥0)

Distinct190
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6284236776
Minimum0
Maximum1
Zeros24
Zeros (%)0.1%
Memory size137.1 KiB
2020-12-03T13:32:24.786856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.31
Q10.48
median0.63
Q30.79
95-th percentile0.93
Maximum1
Range1
Interquartile range (IQR)0.31

Descriptive statistics

Standard deviation0.1930384265
Coefficient of variation (CV)0.3071787925
Kurtosis-0.8218897256
Mean0.6284236776
Median Absolute Deviation (MAD)0.15
Skewness-0.1227604988
Sum11025.065
Variance0.03726383409
MonotocityNot monotonic
2020-12-03T13:32:25.131694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.886573.7%
 
0.836303.6%
 
0.945633.2%
 
0.874902.8%
 
0.74302.5%
 
0.663882.2%
 
0.653872.2%
 
0.693612.1%
 
0.553542.0%
 
0.743411.9%
 
Other values (180)1294373.8%
 
ValueCountFrequency (%) 
0240.1%
 
0.081< 0.1%
 
0.11< 0.1%
 
0.121< 0.1%
 
0.131< 0.1%
 
ValueCountFrequency (%) 
12711.5%
 
0.971< 0.1%
 
0.963< 0.1%
 
0.945633.2%
 
0.933341.9%
 

windspeed
Real number (ℝ≥0)

ZEROS

Distinct122
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1909627593
Minimum0
Maximum0.8507
Zeros2183
Zeros (%)12.4%
Memory size137.1 KiB
2020-12-03T13:32:25.345370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1045
median0.194
Q30.2537
95-th percentile0.4179
Maximum0.8507
Range0.8507
Interquartile range (IQR)0.1492

Descriptive statistics

Standard deviation0.1229107359
Coefficient of variation (CV)0.6436372008
Kurtosis0.6414453942
Mean0.1909627593
Median Absolute Deviation (MAD)0.0895
Skewness0.5858787513
Sum3350.25065
Variance0.01510704899
MonotocityNot monotonic
2020-12-03T13:32:25.533758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0218312.4%
 
0.134317429.9%
 
0.164216969.7%
 
0.19416579.4%
 
0.104516199.2%
 
0.223915138.6%
 
0.089614288.1%
 
0.253712957.4%
 
0.283610486.0%
 
0.29858084.6%
 
Other values (112)255514.6%
 
ValueCountFrequency (%) 
0218312.4%
 
0.04483< 0.1%
 
0.052254< 0.1%
 
0.067152< 0.1%
 
0.08211< 0.1%
 
ValueCountFrequency (%) 
0.85072< 0.1%
 
0.83581< 0.1%
 
0.8062< 0.1%
 
0.80168571431< 0.1%
 
0.77611< 0.1%
 

casual
Real number (ℝ≥0)

ZEROS

Distinct410
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.35838463
Minimum0
Maximum367
Zeros1623
Zeros (%)9.3%
Memory size137.1 KiB
2020-12-03T13:32:25.722814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median16
Q348
95-th percentile138
Maximum367
Range367
Interquartile range (IQR)44

Descriptive statistics

Standard deviation49.18170388
Coefficient of variation (CV)1.390948834
Kurtosis7.643820604
Mean35.35838463
Median Absolute Deviation (MAD)15
Skewness2.510673545
Sum620327.5
Variance2418.839997
MonotocityNot monotonic
2020-12-03T13:32:25.930167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
016239.3%
 
110936.2%
 
28004.6%
 
36974.0%
 
45623.2%
 
55092.9%
 
64472.5%
 
74052.3%
 
83782.2%
 
93482.0%
 
Other values (400)1068260.9%
 
ValueCountFrequency (%) 
016239.3%
 
0.043478260871< 0.1%
 
0.076923076921< 0.1%
 
0.086956521741< 0.1%
 
0.13043478261< 0.1%
 
ValueCountFrequency (%) 
3671< 0.1%
 
3621< 0.1%
 
3611< 0.1%
 
3571< 0.1%
 
3561< 0.1%
 

registered
Real number (ℝ≥0)

HIGH CORRELATION

Distinct868
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.5285283
Minimum0
Maximum886
Zeros24
Zeros (%)0.1%
Memory size137.1 KiB
2020-12-03T13:32:26.157901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q133
median114
Q3219
95-th percentile463
Maximum886
Range886
Interquartile range (IQR)186

Descriptive statistics

Standard deviation151.2184802
Coefficient of variation (CV)0.9914111274
Kurtosis2.775428417
Mean152.5285283
Median Absolute Deviation (MAD)89
Skewness1.565270276
Sum2675960.5
Variance22867.02875
MonotocityNot monotonic
2020-12-03T13:32:26.346738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
43121.8%
 
33111.8%
 
52931.7%
 
62671.5%
 
22541.4%
 
12051.2%
 
72031.2%
 
81911.1%
 
91781.0%
 
111400.8%
 
Other values (858)1519086.6%
 
ValueCountFrequency (%) 
0240.1%
 
12051.2%
 
1.3333333331< 0.1%
 
1.3333333331< 0.1%
 
1.5100.1%
 
ValueCountFrequency (%) 
8861< 0.1%
 
8851< 0.1%
 
8762< 0.1%
 
8711< 0.1%
 
8601< 0.1%
 

cnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct975
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.8869129
Minimum1
Maximum977
Zeros0
Zeros (%)0.0%
Memory size137.1 KiB
2020-12-03T13:32:26.563603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q138
median140
Q3279
95-th percentile562
Maximum977
Range976
Interquartile range (IQR)241

Descriptive statistics

Standard deviation181.278125
Coefficient of variation (CV)0.9648257143
Kurtosis1.43840725
Mean187.8869129
Median Absolute Deviation (MAD)112
Skewness1.285733121
Sum3296288
Variance32861.75859
MonotocityNot monotonic
2020-12-03T13:32:26.779331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
52671.5%
 
42391.4%
 
62371.4%
 
32301.3%
 
22111.2%
 
72021.2%
 
81831.0%
 
11600.9%
 
101550.9%
 
111470.8%
 
Other values (965)1551388.4%
 
ValueCountFrequency (%) 
11600.9%
 
1.3333333331< 0.1%
 
1.57< 0.1%
 
1.6666666671< 0.1%
 
1.6666666671< 0.1%
 
ValueCountFrequency (%) 
9771< 0.1%
 
9761< 0.1%
 
9701< 0.1%
 
9681< 0.1%
 
9671< 0.1%
 

Interactions

2020-12-03T13:32:06.876560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:07.097406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:07.302106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:07.498542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:07.660897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:07.812952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:07.958619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:08.099020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:08.253973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:08.394039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:08.538584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:08.669992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:08.797054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:08.931889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:09.069799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:09.212090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:09.348190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:09.487118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:09.644649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:09.780360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:09.914970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:10.075811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:10.247812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:10.440429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:10.599966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:10.752242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:10.898879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:11.161407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:11.326840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:11.470981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:11.615132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:11.746911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:11.870378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:11.999919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:12.127359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:12.281092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:12.412604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:12.538524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:12.670587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:12.791453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:12.942087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:13.086738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:13.211406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:13.340107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:13.469368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:13.593379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:13.714068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:13.839888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:13.975656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:14.097635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:14.217093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:14.336532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:14.463510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:14.588700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:14.712213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:14.924459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:15.043136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:15.156409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:15.278867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:15.396046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:15.515180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:15.627716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:15.737575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:15.854153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:15.969015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:16.081228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:16.191835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:16.298988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:16.421473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:16.536085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:16.655770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:16.786477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:16.908746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:17.039407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:17.165315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:17.292304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:17.430106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:17.567645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:17.706335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:17.830436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:17.953539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:18.061599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:18.167063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:18.369930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:18.498973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:18.616209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:18.730313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:18.843609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:18.963436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:19.070370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:19.179077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:19.291329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:19.403989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:19.526533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:19.642930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:19.761228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:19.887996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:20.016450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:20.144142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:20.262967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-12-03T13:32:26.986616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-03T13:32:27.275340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-03T13:32:27.529497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-03T13:32:27.762748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-03T13:32:28.101973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-03T13:32:20.543348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-03T13:32:21.121340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

datetimedtedayseasonyrmnthhrholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcnt
02011-01-01 00:00:002011-01-011.00.0100.050.01.00.240.28790.810.00003.013.016.0
12011-01-01 01:00:002011-01-011.00.0110.050.01.00.220.27270.800.00008.032.040.0
22011-01-01 02:00:002011-01-011.00.0120.050.01.00.220.27270.800.00005.027.032.0
32011-01-01 03:00:002011-01-011.00.0130.050.01.00.240.28790.750.00003.010.013.0
42011-01-01 04:00:002011-01-011.00.0140.050.01.00.240.28790.750.00000.01.01.0
52011-01-01 05:00:002011-01-011.00.0150.050.02.00.240.25760.750.08960.01.01.0
62011-01-01 06:00:002011-01-011.00.0160.050.01.00.220.27270.800.00002.00.02.0
72011-01-01 07:00:002011-01-011.00.0170.050.01.00.200.25760.860.00001.02.03.0
82011-01-01 08:00:002011-01-011.00.0180.050.01.00.240.28790.750.00001.07.08.0
92011-01-01 09:00:002011-01-011.00.0190.050.01.00.320.34850.760.00008.06.014.0

Last rows

datetimedtedayseasonyrmnthhrholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcnt
175342012-12-31 14:00:002012-12-311.01.012140.001.02.00.280.27270.450.223962.0185.0247.0
175352012-12-31 15:00:002012-12-311.01.012150.001.02.00.280.28790.450.134369.0246.0315.0
175362012-12-31 16:00:002012-12-311.01.012160.001.02.00.260.25760.480.194030.0184.0214.0
175372012-12-31 17:00:002012-12-311.01.012170.001.02.00.260.28790.480.089614.0150.0164.0
175382012-12-31 18:00:002012-12-311.01.012180.001.02.00.260.27270.480.134310.0112.0122.0
175392012-12-31 19:00:002012-12-311.01.012190.001.02.00.260.25760.600.164211.0108.0119.0
175402012-12-31 20:00:002012-12-311.01.012200.001.02.00.260.25760.600.16428.081.089.0
175412012-12-31 21:00:002012-12-311.01.012210.001.01.00.260.25760.600.16427.083.090.0
175422012-12-31 22:00:002012-12-311.01.012220.001.01.00.260.27270.560.134313.048.061.0
175432012-12-31 23:00:002012-12-311.01.012230.001.01.00.260.27270.650.134312.037.049.0